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Prediction of Mesothelioma Incidence from Asbestos Consumption, A Comparative Study

Prediction of Mesothelioma Incidence from Asbestos Consumption, A Comparative Study. Domyung Paek Seoul National University. Korean Situation. Asbestos industry started operation from 1960’s. Yet mesothelioma incidence stays low around 1-2/million, with male to female ratio of 1.6.

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Prediction of Mesothelioma Incidence from Asbestos Consumption, A Comparative Study

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  1. Prediction of Mesothelioma Incidence from Asbestos Consumption, A Comparative Study Domyung Paek Seoul National University

  2. Korean Situation • Asbestos industry started operation from 1960’s. • Yet mesothelioma incidence stays low around 1-2/million, with male to female ratio of 1.6. • Concerns over what to expect (any increase?), where to expect (asbestos industry locations?) and whom to expect (any specific job or task?)

  3. Industrial Safety and Health Act of Korea, 1981 First Mesothelioma Case in Korea, 1994 Phases of Change EXPANSION PLATEAU FALL

  4. Korean Situation • However, with very scanty data about mesothelioma incidence, future prediction is in murky state. • Need to predict future scenarios based on inter-country comparative study.

  5. Asbestos and Mesothelioma • Usually studied in occupational settings, especially of mining and manufacturing sectors. • However, much bigger problems are found in end-user industries such as construction and ship-building • Per-capita consumption of asbestos can be a fair indicator of asbestos exposure extent in end-user industries

  6. Asbestos and Mesothelioma • The relationship can be studies in two directions • Spatial variation • Between jobs or departments • Between companies or industries • Between different countries • Temporal variation • Between different periods • Between countries of different phases

  7. Asbestos and Mesothelioma • An example of spatial variation study • Per-capita asbestos consumption versus mesothelioma incidence of different countries

  8. Ecological association between asbestos-related diseasesand historical asbestos consumption: an international analysisRo-Ting Lin, Ken Takahashi, Antti Karjalainen, Tsutomu Hoshuyama, Donald Wilson, Takashi Kameda, Chang-Chuan Chan, Chi-Pang Wen, Sugio Furuya, Toshiaki Higashi, Lung-Chang Chien, Megu Ohtaki Lancet 2007; 369: 844–49

  9. Asbestos and Mesothelioma • How about temporal variation study? • Usually future predictions based on age-cohort models without asbestos inputs • As yet, no relationship study between changes of asbestos consumption and mesothelioma incidence

  10. Temporal Variation Study • Analysis of cumulative per-capita asbestos exposure over certain age period versus mesothelioma incidence after certain age of a given birth cohort • When and how long is the best age period of asbestos exposure to explain the changing mesothelioma incidence of different birth cohorts? • How strong is the dose-response of mesothelioma incidence for a given cumulative asbestos exposure?

  11. Temporal Variation Study • Studies from Japan and Netherlands

  12. Temporal Variation Study • Per-capita asbestos consumption • Imported asbestos/population size • Mesothelioma incidence risk of different birth cohorts of 5-10 year periods from 1910-1960 • Age/period/cohort model analysis of mesothelioma incidence data of each country

  13. Analysis • Mesothelioma rate ratio for a specific cohort was calculated based on age-sex specific mesothelioma mortality in certain period, i.e. age-cohort. • Exposure during specific age (period) based on per-capita asbestos consumption was regressed against mesothelioma rate ratio. RR(cohort I) = ∑ ( Per-capita asbestos consumption(i period) * (40- age(i period))2 )

  14. Analysis of exposure age period • Period of from 15 – 25 years old

  15. Analysis of temporal change • Exposures of relatively young age period (15-25 years old) showed the best fit of the data • After the exposure, takes about 30 years to show the elevation of the risk • After the exposure, takes about 50 years to reach the peak of the risk

  16. Prediction of Peak

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